{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 七、集成学习和随机森林"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "果你合并了一组分类器的预测（像分类或者回归），你也会得到一个比单一分类器更好的预测结果。这一组分类器就叫做集成；因此，这个技术就叫做集成学习，一个集成学习算法就叫做集成方法。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "np.random.seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"ensembles\"\n",
    "\n",
    "def image_path(fig_id):\n",
    "    return os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id)\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True):\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(image_path(fig_id) + \".png\", format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Voting classifiers\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "假设你有一个有偏差的硬币，他有 51% 的几率为正面，49% 的几率为背面。如果你实验 1000 次，你会得到差不多 510 次正面，490 次背面，因此大多数都是正面。如果你用数学计算，你会发现在实验 1000 次后，正面概率为 51% 的人比例为 75%。你实验的次数越多，正面的比例越大（例如你试验了 10000 次，总体比例可能性就会达到 97%）。这是因为大数定律 ：当你一直用硬币实验时，正面的比例会越来越接近 51%。图 7-3 展示了始终有偏差的硬币实验。你可以看到当实验次数上升时，正面的概率接近于 51%。最终所有 10 种实验都会收敛到 51%，它们都大于 50%。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "heads_proba = 0.51\n",
    "coin_tosses = (np.random.rand(10000, 10) < heads_proba).astype(np.int32)\n",
    "cumulative_heads_ratio = np.cumsum(coin_tosses, axis=0) / np.arange(1, 10001).reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x252 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,3.5))\n",
    "plt.plot(cumulative_heads_ratio)\n",
    "plt.plot([0, 10000], [0.51, 0.51], \"k--\", linewidth=2, label=\"51%\")\n",
    "plt.plot([0, 10000], [0.5, 0.5], \"k-\", label=\"50%\")\n",
    "plt.xlabel(\"Number of coin tosses\")\n",
    "plt.ylabel(\"Heads ratio\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.axis([0, 10000, 0.42, 0.58])\n",
    "# save_fig(\"law_of_large_numbers_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "假设你创建了一个包含 1000 个分类器的集成模型，其中每个分类器的正确率只有 51%（仅比瞎猜好一点点）。如果你用投票去预测类别，你可能得到 75% 的准确率！然而，这仅仅在所有的分类器都独立运行的很好、不会发生有相关性的错误的情况下才会这样，然而每一个分类器都在同一个数据集上训练，导致其很可能会发生这样的错误。他们可能会犯同一种错误，所以也会有很多票投给了错误类别导致集成的准确率下降。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来的代码创建和训练了在 sklearn 中的投票分类器。这个分类器由三个不同的分类器组成（训练集是第五章中的 moons 数据集）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import make_moons\n",
    "\n",
    "X, y = make_moons(n_samples=500, noise=0.30, random_state=42)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('lr', LogisticRegression(random_state=42)),\n",
       "                             ('rf', RandomForestClassifier(random_state=42)),\n",
       "                             ('svc', SVC(random_state=42))])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "log_clf = LogisticRegression(random_state=42)\n",
    "rnd_clf = RandomForestClassifier(random_state=42)\n",
    "svm_clf = SVC(random_state=42)\n",
    "\n",
    "voting_clf = VotingClassifier(\n",
    "    estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n",
    "    voting='hard')\n",
    "voting_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "让我们看一下在测试集上的准确率："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression 0.864\n",
      "RandomForestClassifier 0.896\n",
      "SVC 0.896\n",
      "VotingClassifier 0.912\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "for clf in (log_clf, rnd_clf, svm_clf, voting_clf):\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print(clf.__class__.__name__, accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "你看！投票分类器比其他单独的分类器表现的都要好。\n",
    "\n",
    "如果所有的分类器都能够预测类别的概率（例如他们有一个predict_proba()方法），那么你就可以让 sklearn 以最高的类概率来预测这个类，平均在所有的分类器上。这种方式叫做软投票。他经常比硬投票表现的更好，因为它给予高自信的投票更大的权重。你可以通过把voting=\"hard\"设置为voting=\"soft\"来保证分类器可以预测类别概率。然而这不是 SVC 类的分类器默认的选项，所以你需要把它的probability hyperparameter设置为True（这会使 SVC 使用交叉验证去预测类别概率，其降低了训练速度，但会添加predict_proba()方法）。如果你修改了之前的代码去使用软投票，你会发现投票分类器正确率高达 91%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('lr', LogisticRegression(random_state=42)),\n",
       "                             ('rf', RandomForestClassifier(random_state=42)),\n",
       "                             ('svc', SVC(probability=True, random_state=42))],\n",
       "                 voting='soft')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_clf = LogisticRegression(random_state=42)\n",
    "rnd_clf = RandomForestClassifier(random_state=42)\n",
    "svm_clf = SVC(probability=True, random_state=42)\n",
    "\n",
    "voting_clf = VotingClassifier(\n",
    "    estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n",
    "    voting='soft')\n",
    "voting_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression 0.864\n",
      "RandomForestClassifier 0.896\n",
      "SVC 0.896\n",
      "VotingClassifier 0.92\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "for clf in (log_clf, rnd_clf, svm_clf, voting_clf):\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print(clf.__class__.__name__, accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bagging ensembles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 在 sklearn 中的 Bagging 和 Pasting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "就像之前讲到的，可以通过使用不同的训练算法去得到一些不同的分类器。另一种方法就是对每一个分类器都使用相同的训练算法，但是在不同的训练集上去训练它们。有放回采样被称为装袋（Bagging，是 bootstrap aggregating 的缩写）。无放回采样称为粘贴（pasting）。\n",
    "换句话说，Bagging 和 Pasting 都允许在多个分类器上对训练集进行多次采样，但只有 Bagging 允许对同一种分类器上对训练集进行进行多次采样"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当所有的分类器被训练后，集成可以通过对所有分类器结果的简单聚合来对新的实例进行预测。聚合函数通常对分类是统计模式（例如硬投票分类器）或者对回归是平均。每一个单独的分类器在如果在原始训练集上都是高偏差，但是聚合降低了偏差和方差。通常情况下，集成的结果是有一个相似的偏差，但是对比与在原始训练集上的单一分类器来讲有更小的方差。\n",
    "\n",
    "正如你在图 7-4 上所看到的，分类器可以通过不同的 CPU 核或其他的服务器一起被训练。相似的，分类器也可以一起被制作。这就是为什么 Bagging 和 Pasting 是如此流行的原因之一：它们的可扩展性很好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "bag_clf = BaggingClassifier(\n",
    "    DecisionTreeClassifier(random_state=42), n_estimators=500,\n",
    "    max_samples=100, bootstrap=True, n_jobs=-1, random_state=42)\n",
    "bag_clf.fit(X_train, y_train)\n",
    "y_pred = bag_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据这个 obb 评估，BaggingClassifier可以再测试集上达到 93.1%的准确率，让我们修改一下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.904\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.856\n"
     ]
    }
   ],
   "source": [
    "tree_clf = DecisionTreeClassifier(random_state=42)\n",
    "tree_clf.fit(X_train, y_train)\n",
    "y_pred_tree = tree_clf.predict(X_test)\n",
    "print(accuracy_score(y_test, y_pred_tree))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "sklearn 为 Bagging 和 Pasting 提供了一个简单的 API：BaggingClassifier类（或者对于回归可以是BaggingRegressor。接下来的代码训练了一个 500 个决策树分类器的集成，每一个都是在数据集上有放回采样 100 个训练实例下进行训练（这是 Bagging 的例子，如果你想尝试 Pasting，就设置bootstrap=False）。n_jobs参数告诉 sklearn 用于训练和预测所需要 CPU 核的数量。（-1 代表着 sklearn 会使用所有空闲核）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "def plot_decision_boundary(clf, X, y, axes=[-1.5, 2.5, -1, 1.5], alpha=0.5, contour=True):\n",
    "    x1s = np.linspace(axes[0], axes[1], 100)\n",
    "    x2s = np.linspace(axes[2], axes[3], 100)\n",
    "    x1, x2 = np.meshgrid(x1s, x2s)\n",
    "    X_new = np.c_[x1.ravel(), x2.ravel()]\n",
    "    y_pred = clf.predict(X_new).reshape(x1.shape)\n",
    "    custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\n",
    "    plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap, linewidth=10)\n",
    "    if contour:\n",
    "        custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])\n",
    "        plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)\n",
    "    plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", alpha=alpha)\n",
    "    plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", alpha=alpha)\n",
    "    plt.axis(axes)\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=18)\n",
    "    plt.ylabel(r\"$x_2$\", fontsize=18, rotation=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,4))\n",
    "plt.subplot(121)\n",
    "plot_decision_boundary(tree_clf, X, y)\n",
    "plt.title(\"Decision Tree\", fontsize=14)\n",
    "plt.subplot(122)\n",
    "plot_decision_boundary(bag_clf, X, y)\n",
    "plt.title(\"Decision Trees with Bagging\", fontsize=14)\n",
    "# save_fig(\"decision_tree_without_and_with_bagging_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "图 7-5 对比了单一决策树的决策边界和 Bagging 集成 500 个树的决策边界，两者都在 moons 数据集上训练。正如你所看到的，集成的分类比起单一决策树的分类产生情况更好：集成有一个可比较的偏差但是有一个较小的方差（它在训练集上的错误数目大致相同，但决策边界较不规则）。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bootstrap 在每个预测器被训练的子集中引入了更多的分集，所以 Bagging 结束时的偏差比 Pasting 更高，但这也意味着预测因子最终变得不相关，从而减少了集合的方差。总体而言，Bagging 通常会导致更好的模型，这就解释了为什么它通常是首选的。然而，如果你有空闲时间和 CPU 功率，可以使用交叉验证来评估 Bagging 和 Pasting 哪一个更好。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "因为在训练中分类器从来没有看到过 oob 实例，所以它可以在这些实例上进行评估，而不需要单独的验证集或交叉验证。你可以拿出每一个分类器的 oob 来评估集成本身。\n",
    "\n",
    "在 sklearn 中，你可以在训练后需要创建一个BaggingClassifier来自动评估时设置oob_score=True来自动评估。接下来的代码展示了这个操作。评估结果通过变量oob_score_来显示："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Out-of-Bag 评价"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于 Bagging 来说，一些实例可能被一些分类器重复采样，但其他的有可能不会被采样。BaggingClassifier默认采样。BaggingClassifier默认是有放回的采样m个实例 （bootstrap=True），其中m是训练集的大小，这意味着平均下来只有 63%的训练实例被每个分类器采样，剩下的 37%个没有被采样的训练实例就叫做 Out-of-Bag 实例。注意对于每一个的分类器它们的 37% 不是相同的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8933333333333333"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_clf = BaggingClassifier(DecisionTreeClassifier(), n_estimators=500,bootstrap=True, n_jobs=-1, oob_score=True)\n",
    "bag_clf.fit(X_train, y_train) \n",
    "bag_clf.oob_score_ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据这个 obb 评估，BaggingClassifier可以再测试集上达到 93.1%的准确率，让我们修改一下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.912"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score \n",
    "y_pred = bag_clf.predict(X_test) \n",
    "accuracy_score(y_test, y_pred) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们在测试集上得到了 93.6% 的准确率，足够接近了！\n",
    "\n",
    "对于每个训练实例 oob 决策函数也可通过oob_decision_function_变量来展示。在这种情况下（当基决策器有predict_proba()时）决策函数会对每个训练实例返回类别概率。例如，oob 评估预测第二个训练实例有 60.6% 的概率属于正类（39.4% 属于负类）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.34536082, 0.65463918],\n",
       "       [0.4       , 0.6       ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.07303371, 0.92696629],\n",
       "       [0.4       , 0.6       ],\n",
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       "       [0.27807487, 0.72192513],\n",
       "       [0.9952381 , 0.0047619 ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
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       "       [0.80722892, 0.19277108],\n",
       "       [0.97311828, 0.02688172],\n",
       "       [1.        , 0.        ],\n",
       "       [0.70857143, 0.29142857],\n",
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       "       [0.        , 1.        ],\n",
       "       [0.90862944, 0.09137056],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.82446809, 0.17553191],\n",
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       "       [0.        , 1.        ],\n",
       "       [0.87027027, 0.12972973],\n",
       "       [0.83957219, 0.16042781],\n",
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       "       [1.        , 0.        ],\n",
       "       [0.99421965, 0.00578035],\n",
       "       [1.        , 0.        ],\n",
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       "       [0.04      , 0.96      ],\n",
       "       [0.96666667, 0.03333333],\n",
       "       [0.93467337, 0.06532663],\n",
       "       [1.        , 0.        ],\n",
       "       [0.55080214, 0.44919786],\n",
       "       [1.        , 0.        ],\n",
       "       [0.00588235, 0.99411765],\n",
       "       [0.97752809, 0.02247191],\n",
       "       [0.02717391, 0.97282609],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.94871795, 0.05128205],\n",
       "       [0.        , 1.        ],\n",
       "       [0.11363636, 0.88636364],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.00518135, 0.99481865],\n",
       "       [1.        , 0.        ],\n",
       "       [0.16853933, 0.83146067],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.38916256, 0.61083744],\n",
       "       [0.07177033, 0.92822967],\n",
       "       [0.24712644, 0.75287356],\n",
       "       [1.        , 0.        ],\n",
       "       [0.97802198, 0.02197802],\n",
       "       [0.22857143, 0.77142857],\n",
       "       [0.98369565, 0.01630435],\n",
       "       [0.        , 1.        ],\n",
       "       [0.00518135, 0.99481865],\n",
       "       [1.        , 0.        ],\n",
       "       [0.97237569, 0.02762431],\n",
       "       [0.38461538, 0.61538462],\n",
       "       [0.97826087, 0.02173913],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.03398058, 0.96601942],\n",
       "       [0.99431818, 0.00568182],\n",
       "       [1.        , 0.        ],\n",
       "       [0.05714286, 0.94285714],\n",
       "       [0.63783784, 0.36216216]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_clf.oob_decision_function_ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 随机贴片与随机子空间"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "BaggingClassifier也支持采样特征。它被两个超参数max_features和bootstrap_features控制。他们的工作方式和max_samples和bootstrap一样，但这是对于特征采样而不是实例采样。因此，每一个分类器都会被在随机的输入特征内进行训练。\n",
    "\n",
    "当你在处理高维度输入下（例如图片）此方法尤其有效。对训练实例和特征的采样被叫做随机贴片。保留了所有的训练实例（例如bootstrap=False和max_samples=1.0），但是对特征采样（bootstrap_features=True并且/或者max_features小于 1.0）叫做随机子空间。\n",
    "\n",
    "采样特征导致更多的预测多样性，用高偏差换低方差。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "正如我们所讨论的，随机森林是决策树的一种集成，通常是通过 bagging 方法（有时是 pasting 方法）进行训练，通常用max_samples设置为训练集的大小。与建立一个BaggingClassifier然后把它放入 DecisionTreeClassifier 相反，你可以使用更方便的也是对决策树优化够的RandomForestClassifier（对于回归是RandomForestRegressor）。接下来的代码训练了带有 500 个树（每个被限制为 16 叶子结点）的决策森林，使用所有空闲的 CPU 核："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "bag_clf.fit(X_train, y_train)\n",
    "y_pred = bag_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rnd_clf = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16, n_jobs=-1, random_state=42)\n",
    "rnd_clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred_rf = rnd_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "随机森林算法在树生长时引入了额外的随机；与在节点分裂时需要找到最好分裂特征相反（详见第六章），它在一个随机的特征集中找最好的特征。它导致了树的差异性，并且再一次用高偏差换低方差，总的来说是一个更好的模型。以下是BaggingClassifier大致相当于之前的randomforestclassifier："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "bag_clf = BaggingClassifier(\n",
    "    DecisionTreeClassifier(splitter=\"random\", max_leaf_nodes=16, random_state=42),\n",
    "    n_estimators=500, max_samples=1.0, bootstrap=True, n_jobs=-1, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.976"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(y_pred == y_pred_rf) / len(y_pred)  # almost identical predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 极端随机树"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当你在随机森林上生长树时，在每个结点分裂时只考虑随机特征集上的特征（正如之前讨论过的一样）。相比于找到更好的特征我们可以通过使用对特征使用随机阈值使树更加随机（像规则决策树一样）。\n",
    "\n",
    "这种极端随机的树被简称为 Extremely Randomized Trees（极端随机树），或者更简单的称为 Extra-Tree。再一次用高偏差换低方差。它还使得 Extra-Tree 比规则的随机森林更快地训练，因为在每个节点上找到每个特征的最佳阈值是生长树最耗时的任务之一。\n",
    "\n",
    "你可以使用 sklearn 的ExtraTreesClassifier来创建一个 Extra-Tree 分类器。他的 API 跟RandomForestClassifier是相同的，相似的， ExtraTreesRegressor 跟RandomForestRegressor也是相同的 API。\n",
    "\n",
    "我们很难去分辨ExtraTreesClassifier和RandomForestClassifier到底哪个更好。通常情况下是通过交叉验证来比较它们（使用网格搜索调整超参数）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "\n",
    "ext_clf = ExtraTreesClassifier(n_estimators=500, max_leaf_nodes=16, n_jobs=-1, random_state=42)\n",
    "ext_clf.fit(X_train, y_train)\n",
    "y_pred_ext = ext_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.968"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(y_pred_ext == y_pred_rf) / len(y_pred_ext)  # almost identical predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征重要度"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果你观察一个单一决策树，重要的特征会出现在更靠近根部的位置，而不重要的特征会经常出现在靠近叶子的位置。因此我们可以通过计算一个特征在森林的全部树中出现的平均深度来预测特征的重要性。sklearn 在训练后会自动计算每个特征的重要度。你可以通过feature_importances_变量来查看结果。例如如下代码在 iris 数据集（第四章介绍）上训练了一个RandomForestClassifier模型，然后输出了每个特征的重要性。看来，最重要的特征是花瓣长度（44%）和宽度（42%），而萼片长度和宽度相对比较是不重要的（分别为 11% 和 2%）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sepal length (cm) 0.11249225099876375\n",
      "sepal width (cm) 0.02311928828251033\n",
      "petal length (cm) 0.4410304643639577\n",
      "petal width (cm) 0.4233579963547682\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()\n",
    "rnd_clf = RandomForestClassifier(n_estimators=500, n_jobs=-1, random_state=42)\n",
    "rnd_clf.fit(iris[\"data\"], iris[\"target\"])\n",
    "for name, score in zip(iris[\"feature_names\"], rnd_clf.feature_importances_):\n",
    "    print(name, score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.11249225, 0.02311929, 0.44103046, 0.423358  ])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_clf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4))\n",
    "\n",
    "for i in range(15):\n",
    "    tree_clf = DecisionTreeClassifier(max_leaf_nodes=16, random_state=42 + i)\n",
    "    indices_with_replacement = np.random.randint(0, len(X_train), len(X_train))\n",
    "    tree_clf.fit(X[indices_with_replacement], y[indices_with_replacement])\n",
    "    plot_decision_boundary(tree_clf, X, y, axes=[-1.5, 2.5, -1, 1.5], alpha=0.02, contour=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "相似的，如果你在 MNIST 数据及上训练随机森林分类器（在第三章上介绍），然后画出每个像素的重要性，你可以得到图 7-6 的图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "mnist_data = fetch_openml(\"mnist_784\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_clf = RandomForestClassifier(random_state=42)\n",
    "rnd_clf.fit(mnist_data[\"data\"], mnist_data[\"target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_digit(data):\n",
    "    image = data.reshape(28, 28)\n",
    "    plt.imshow(image, cmap = matplotlib.cm.hot,\n",
    "               interpolation=\"nearest\")\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_digit(rnd_clf.feature_importances_)\n",
    "\n",
    "cbar = plt.colorbar(ticks=[rnd_clf.feature_importances_.min(), rnd_clf.feature_importances_.max()])\n",
    "cbar.ax.set_yticklabels(['Not important', 'Very important'])\n",
    "\n",
    "# save_fig(\"mnist_feature_importance_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "随机森林可以非常方便快速得了解哪些特征实际上是重要的，特别是你需要进行特征选择的时候。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Boosting 假设增强"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "提升（Boosting，最初称为假设增强）指的是可以将几个弱学习者组合成强学习者的集成方法。对于大多数的提升方法的思想就是按顺序去训练分类器，每一个都要尝试修正前面的分类。现如今已经有很多的提升方法了，但最著名的就是 Adaboost（适应性提升，是 Adaptive Boosting 的简称） 和 Gradient Boosting（梯度提升）。让我们先从 Adaboost 说起。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adaboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:7: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  import sys\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:7: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  import sys\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:7: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  import sys\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:7: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  import sys\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:7: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  import sys\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:7: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  import sys\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:7: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  import sys\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:7: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  import sys\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:15: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = len(X_train)\n",
    "\n",
    "plt.figure(figsize=(11, 4))\n",
    "for subplot, learning_rate in ((121, 1), (122, 0.5)):\n",
    "    sample_weights = np.ones(m)\n",
    "    for i in range(5):\n",
    "        plt.subplot(subplot)\n",
    "        svm_clf = SVC(kernel=\"rbf\", C=0.05, random_state=42)\n",
    "        svm_clf.fit(X_train, y_train, sample_weight=sample_weights)\n",
    "        y_pred = svm_clf.predict(X_train)\n",
    "        sample_weights[y_pred != y_train] *= (1 + learning_rate)\n",
    "        plot_decision_boundary(svm_clf, X, y, alpha=0.2)\n",
    "        plt.title(\"learning_rate = {}\".format(learning_rate), fontsize=16)\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.text(-0.7, -0.65, \"1\", fontsize=14)\n",
    "plt.text(-0.6, -0.10, \"2\", fontsize=14)\n",
    "plt.text(-0.5,  0.10, \"3\", fontsize=14)\n",
    "plt.text(-0.4,  0.55, \"4\", fontsize=14)\n",
    "plt.text(-0.3,  0.90, \"5\", fontsize=14)\n",
    "# save_fig(\"boosting_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "显示连续五次预测的 moons 数据集的决策边界（在本例中，每一个分类器都是高度正则化带有 RBF 核的 SVM）。第一个分类器误分类了很多实例，所以它们的权重被提升了。第二个分类器因此对这些误分类的实例分类效果更好，以此类推。右边的图代表了除了学习率减半外（误分类实例权重每次迭代上升一半）相同的预测序列。你可以看出，序列学习技术与梯度下降很相似，除了调整单个预测因子的参数以最小化代价函数之外，AdaBoost 增加了集合的预测器，逐渐使其更好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['base_estimator_',\n",
       " 'classes_',\n",
       " 'estimator_errors_',\n",
       " 'estimator_weights_',\n",
       " 'estimators_',\n",
       " 'feature_importances_',\n",
       " 'n_classes_',\n",
       " 'n_features_in_']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(m for m in dir(ada_clf) if not m.startswith(\"_\") and m.endswith(\"_\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "sklearn 通常使用 Adaboost 的多分类版本 SAMME（这就代表了 分段加建模使用多类指数损失函数）。如果只有两类别，那么 SAMME 是与 Adaboost 相同的。如果分类器可以预测类别概率（例如如果它们有predict_proba()），如果 sklearn 可以使用 SAMME 叫做SAMME.R的变量（R 代表“REAL”），这种依赖于类别概率的通常比依赖于分类器的更好。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来的代码训练了使用 sklearn 的AdaBoostClassifier基于 200 个决策树桩 Adaboost 分类器（正如你说期待的，对于回归也有AdaBoostRegressor）。一个决策树桩是max_depth=1的决策树-换句话说，是一个单一的决策节点加上两个叶子结点。这就是AdaBoostClassifier的默认基分类器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=1),\n",
       "                   learning_rate=0.5, n_estimators=200, random_state=42)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "ada_clf = AdaBoostClassifier(\n",
    "    DecisionTreeClassifier(max_depth=1), n_estimators=200,\n",
    "    algorithm=\"SAMME.R\", learning_rate=0.5, random_state=42)\n",
    "ada_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(ada_clf, X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果你的 Adaboost 集成过拟合了训练集，你可以尝试减少基分类器的数量或者对基分类器使用更强的正则化。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Boosting 梯度提升\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "另一个非常著名的提升算法是梯度提升。与 Adaboost 一样，梯度提升也是通过向集成中逐步增加分类器运行的，每一个分类器都修正之前的分类结果。然而，它并不像 Adaboost 那样每一次迭代都更改实例的权重，这个方法是去使用新的分类器去拟合前面分类器预测的残差 。\n",
    "\n",
    "让我们通过一个使用决策树当做基分类器的简单的回归例子（回归当然也可以使用梯度提升）。这被叫做梯度提升回归树（GBRT，Gradient Tree Boosting 或者 Gradient Boosted Regression Trees）。首先我们用DecisionTreeRegressor去拟合训练集（例如一个有噪二次训练集）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "X = np.random.rand(100, 1) - 0.5\n",
    "y = 3*X[:, 0]**2 + 0.05 * np.random.randn(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=2, random_state=42)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg1 = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
    "tree_reg1.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在在第一个分类器的残差上训练第二个分类器："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=2, random_state=42)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y2 = y - tree_reg1.predict(X)\n",
    "tree_reg2 = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
    "tree_reg2.fit(X, y2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "随后在第二个分类器的残差上训练第三个分类器："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=2, random_state=42)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y3 = y2 - tree_reg2.predict(X)\n",
    "tree_reg3 = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
    "tree_reg3.fit(X, y3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在我们有了一个包含三个回归器的集成。它可以通过集成所有树的预测来在一个新的实例上进行预测。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_new = np.array([[0.8]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.75026781])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = sum(tree.predict(X_new) for tree in (tree_reg1, tree_reg2, tree_reg3))\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x792 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_predictions(regressors, X, y, axes, label=None, style=\"r-\", data_style=\"b.\", data_label=None):\n",
    "    x1 = np.linspace(axes[0], axes[1], 500)\n",
    "    y_pred = sum(regressor.predict(x1.reshape(-1, 1)) for regressor in regressors)\n",
    "    plt.plot(X[:, 0], y, data_style, label=data_label)\n",
    "    plt.plot(x1, y_pred, style, linewidth=2, label=label)\n",
    "    if label or data_label:\n",
    "        plt.legend(loc=\"upper center\", fontsize=16)\n",
    "    plt.axis(axes)\n",
    "\n",
    "plt.figure(figsize=(11,11))\n",
    "\n",
    "plt.subplot(321)\n",
    "plot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h_1(x_1)$\", style=\"g-\", data_label=\"Training set\")\n",
    "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n",
    "plt.title(\"Residuals and tree predictions\", fontsize=16)\n",
    "\n",
    "plt.subplot(322)\n",
    "plot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h(x_1) = h_1(x_1)$\", data_label=\"Training set\")\n",
    "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n",
    "plt.title(\"Ensemble predictions\", fontsize=16)\n",
    "\n",
    "plt.subplot(323)\n",
    "plot_predictions([tree_reg2], X, y2, axes=[-0.5, 0.5, -0.5, 0.5], label=\"$h_2(x_1)$\", style=\"g-\", data_style=\"k+\", data_label=\"Residuals\")\n",
    "plt.ylabel(\"$y - h_1(x_1)$\", fontsize=16)\n",
    "\n",
    "plt.subplot(324)\n",
    "plot_predictions([tree_reg1, tree_reg2], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h(x_1) = h_1(x_1) + h_2(x_1)$\")\n",
    "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n",
    "\n",
    "plt.subplot(325)\n",
    "plot_predictions([tree_reg3], X, y3, axes=[-0.5, 0.5, -0.5, 0.5], label=\"$h_3(x_1)$\", style=\"g-\", data_style=\"k+\")\n",
    "plt.ylabel(\"$y - h_1(x_1) - h_2(x_1)$\", fontsize=16)\n",
    "plt.xlabel(\"$x_1$\", fontsize=16)\n",
    "\n",
    "plt.subplot(326)\n",
    "plot_predictions([tree_reg1, tree_reg2, tree_reg3], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h(x_1) = h_1(x_1) + h_2(x_1) + h_3(x_1)$\")\n",
    "plt.xlabel(\"$x_1$\", fontsize=16)\n",
    "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n",
    "\n",
    "# save_fig(\"gradient_boosting_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "图 7-9 在左栏展示了这三个树的预测，在右栏展示了集成的预测。在第一行，集成只有一个树，所以它与第一个树的预测相似。在第二行，一个新的树在第一个树的残差上进行训练。在右边栏可以看出集成的预测等于前两个树预测的和。相同的，在第三行另一个树在第二个数的残差上训练。你可以看到集成的预测会变的更好。\n",
    "\n",
    "我们可以使用 sklean 中的GradientBoostingRegressor来训练 GBRT 集成。与RandomForestClassifier相似，它也有超参数去控制决策树的生长（例如max_depth，min_samples_leaf等等），也有超参数去控制集成训练，例如基分类器的数量（n_estimators）。接下来的代码创建了与之前相同的集成："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(learning_rate=1.0, max_depth=2, n_estimators=3,\n",
       "                          random_state=42)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "gbrt = GradientBoostingRegressor(max_depth=2, n_estimators=3, learning_rate=1.0, random_state=42)\n",
    "gbrt.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "超参数learning_rate 确立了每个树的贡献。如果你把它设置为一个很小的树，例如 0.1，在集成中就需要更多的树去拟合训练集，但预测通常会更好。这个正则化技术叫做 shrinkage。图 7-10 展示了两个在低学习率上训练的 GBRT 集成：其中左面是一个没有足够树去拟合训练集的树，右面是有过多的树过拟合训练集的树。\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(max_depth=2, n_estimators=200, random_state=42)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbrt_slow = GradientBoostingRegressor(max_depth=2, n_estimators=200, learning_rate=0.1, random_state=42)\n",
    "gbrt_slow.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plot_predictions([gbrt], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"Ensemble predictions\")\n",
    "plt.title(\"learning_rate={}, n_estimators={}\".format(gbrt.learning_rate, gbrt.n_estimators), fontsize=14)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_predictions([gbrt_slow], X, y, axes=[-0.5, 0.5, -0.1, 0.8])\n",
    "plt.title(\"learning_rate={}, n_estimators={}\".format(gbrt_slow.learning_rate, gbrt_slow.n_estimators), fontsize=14)\n",
    "\n",
    "# save_fig(\"gbrt_learning_rate_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Boosting with Early stopping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了找到树的最优数量，你可以使用早停技术（第四章讨论）。最简单使用这个技术的方法就是使用staged_predict()：它在训练的每个阶段（用一棵树，两棵树等）返回一个迭代器。接下来的代码用 120 个树训练了一个 GBRT 集成，然后在训练的每个阶段验证错误以找到树的最佳数量，最后使用 GBRT 树的最优数量训练另一个集成："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(max_depth=2, n_estimators=55, random_state=42)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, random_state=49)\n",
    "\n",
    "gbrt = GradientBoostingRegressor(max_depth=2, n_estimators=120, random_state=42)\n",
    "gbrt.fit(X_train, y_train)\n",
    "\n",
    "errors = [mean_squared_error(y_val, y_pred)\n",
    "          for y_pred in gbrt.staged_predict(X_val)]\n",
    "bst_n_estimators = np.argmin(errors)\n",
    "\n",
    "gbrt_best = GradientBoostingRegressor(max_depth=2,n_estimators=bst_n_estimators, random_state=42)\n",
    "gbrt_best.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_error = np.min(errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(errors, \"b.-\")\n",
    "plt.plot([bst_n_estimators, bst_n_estimators], [0, min_error], \"k--\")\n",
    "plt.plot([0, 120], [min_error, min_error], \"k--\")\n",
    "plt.plot(bst_n_estimators, min_error, \"ko\")\n",
    "plt.text(bst_n_estimators, min_error*1.2, \"Minimum\", ha=\"center\", fontsize=14)\n",
    "plt.axis([0, 120, 0, 0.01])\n",
    "plt.xlabel(\"Number of trees\")\n",
    "plt.title(\"Validation error\", fontsize=14)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_predictions([gbrt_best], X, y, axes=[-0.5, 0.5, -0.1, 0.8])\n",
    "plt.title(\"Best model (%d trees)\" % bst_n_estimators, fontsize=14)\n",
    "\n",
    "# save_fig(\"early_stopping_gbrt_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "你也可以早早的停止训练来实现早停（与先在一大堆树中训练，然后再回头去找最优数目相反）。你可以通过设置warm_start=True来实现 ，这使得当fit()方法被调用时 sklearn 保留现有树，并允许增量训练。接下来的代码在当一行中的五次迭代验证错误没有改善时会停止训练："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbrt = GradientBoostingRegressor(max_depth=2, warm_start=True, random_state=42)\n",
    "\n",
    "min_val_error = float(\"inf\")\n",
    "error_going_up = 0\n",
    "for n_estimators in range(1, 120):\n",
    "    gbrt.n_estimators = n_estimators\n",
    "    gbrt.fit(X_train, y_train)\n",
    "    y_pred = gbrt.predict(X_val)\n",
    "    val_error = mean_squared_error(y_val, y_pred)\n",
    "    if val_error < min_val_error:\n",
    "        min_val_error = val_error\n",
    "        error_going_up = 0\n",
    "    else:\n",
    "        error_going_up += 1\n",
    "        if error_going_up == 5:\n",
    "            break  # early stopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "61\n"
     ]
    }
   ],
   "source": [
    "print(gbrt.n_estimators)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GradientBoostingRegressor也支持指定用于训练每棵树的训练实例比例的超参数subsample。例如如果subsample=0.25，那么每个树都会在 25% 随机选择的训练实例上训练。你现在也能猜出来，这也是个高偏差换低方差的作用。它同样也加速了训练。这个技术叫做随机梯度提升。\n",
    "\n",
    "也可能对其他损失函数使用梯度提升。这是由损失超参数控制（见 sklearn 文档）。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
